IDENTIFICATION OF ARI MODEL WITH APPLICATIONS TO ON-LINE TREND DETECTIONS.

Shu ichi Adachi, Akira Sano, Koh ichi Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the recursive adaptive algorithms for rapidly detecting various stochastic trends in signals by modeling them as the autoregressive integrated(ARI) process. In order to determine the degree of differencing which represents the changing rate of nonstationary trend components, we derive a new criterion on a basis of the concept of the AIC. The generalized gradient(GG) algorithm and the normalized least squares lattice(NLSL) filter are utilized to identify coefficient parameters in the ARI model in an on-line manner. The effectiveness of the algorithms is examined through numerical simulation using actual data.

Original languageEnglish
Title of host publicationIFAC Proceedings Series
PublisherPergamon Press
Pages1491-1496
Number of pages6
Edition7
ISBN (Print)0080325424
Publication statusPublished - 1985 Dec 1

Publication series

NameIFAC Proceedings Series
Number7
ISSN (Print)0741-1146

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'IDENTIFICATION OF ARI MODEL WITH APPLICATIONS TO ON-LINE TREND DETECTIONS.'. Together they form a unique fingerprint.

  • Cite this

    Adachi, S. I., Sano, A., & Hashimoto, K. I. (1985). IDENTIFICATION OF ARI MODEL WITH APPLICATIONS TO ON-LINE TREND DETECTIONS. In IFAC Proceedings Series (7 ed., pp. 1491-1496). (IFAC Proceedings Series; No. 7). Pergamon Press.